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⏰ DEADLINE EXTENDED⏰

For the special issue "Recognising diversity of cells, systems & individuals to advance arrhythmia therapy"📖

🔗 For full information & how to submit: physoc.onlinelibrary.wiley.com/hub/journal/...

#Arrhythmia #ComputerModelling #PrecisionMedicine

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This study published in #RSOS demonstrates how satellite imagery and mapping technologies enable scientists to monitor environmental factors, such as temperature, rainfall and land use, that influence disease outbreaks buff.ly/kejDpvZ | #ComputerModelling #Epidemiology #EnvironmentalScience

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New research from #ProcA: An emergent optimal resource allocation for climate resilience of transport infrastructure networks buff.ly/NiE8FyR | #CivilEngineering #ComputerModelling #MathematicalModelling

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New from #ProcA: Uncertainty in the Manning's roughness coefficient in multi-level #simulations of future tsunamis in Sumatra buff.ly/VgQrBsy | #ComputerModelling #Geophysics #Statistics

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Computer modeling in physics and physics education

1. What is the universality of the mathematical modeling method?

2. What mathematical methods are used in solving problems by the mathematical modeling method?

3. How to verify the validity of a numerical method?

4. Give examples of problems that do not have an analytical solution. How are computational and analytical methods combined in mathematical modeling?

5. What are the features of modeling problems described by nonlinear equations?

6. Give examples of problems where regular and chaotic solutions arise with parameter changes?

7. Give examples demonstrating self-organization processes in complex nonlinear systems.

8. The role of computational physics in science and physical education.

9. Computational experiment and its relationship with natural experiment.

10. Tools for implementing a computational experiment available for use in school.

Computer modeling in physics and physics education 1. What is the universality of the mathematical modeling method? 2. What mathematical methods are used in solving problems by the mathematical modeling method? 3. How to verify the validity of a numerical method? 4. Give examples of problems that do not have an analytical solution. How are computational and analytical methods combined in mathematical modeling? 5. What are the features of modeling problems described by nonlinear equations? 6. Give examples of problems where regular and chaotic solutions arise with parameter changes? 7. Give examples demonstrating self-organization processes in complex nonlinear systems. 8. The role of computational physics in science and physical education. 9. Computational experiment and its relationship with natural experiment. 10. Tools for implementing a computational experiment available for use in school.

11. Methods and didactic goals of using computer modeling and virtual experiments in school physics lessons.

12. Combining natural and computational experiments in teaching physics at school (using a specific example).

13. Difficulties in using computer modeling and virtual experiments in school.

14. The role of the teacher in students' learning of computer modeling and virtual experiments.

15. Measurement of physical quantities and processing of measurement results. Basic concepts. Physical quantity and its measurement. Direct and indirect measurements.

16. Measurement uncertainties: instrumental, systematic, random, methodological, gross errors, etc.

17. Processing results of school physics experiments. Teaching methodology for accounting for uncertainties in measurements across different grade levels.

18. Processing of school physics experiment results. Processing of direct and indirect measurement results — main stages and methodology for teaching students.

11. Methods and didactic goals of using computer modeling and virtual experiments in school physics lessons. 12. Combining natural and computational experiments in teaching physics at school (using a specific example). 13. Difficulties in using computer modeling and virtual experiments in school. 14. The role of the teacher in students' learning of computer modeling and virtual experiments. 15. Measurement of physical quantities and processing of measurement results. Basic concepts. Physical quantity and its measurement. Direct and indirect measurements. 16. Measurement uncertainties: instrumental, systematic, random, methodological, gross errors, etc. 17. Processing results of school physics experiments. Teaching methodology for accounting for uncertainties in measurements across different grade levels. 18. Processing of school physics experiment results. Processing of direct and indirect measurement results — main stages and methodology for teaching students.

19. Basics of data visualization. Analysis of numerical data. Methods of data organization. Data transformation. Principles of visualization in diagrams, rules for constructing graphs.

20. Qualitative data analysis. Key techniques and features of approaches.

19. Basics of data visualization. Analysis of numerical data. Methods of data organization. Data transformation. Principles of visualization in diagrams, rules for constructing graphs. 20. Qualitative data analysis. Key techniques and features of approaches.

My students are having their exam this Wednesday. Fingers crossed they pass! If they don't, they'll be expelled the very next day (after one last try).

The drama of the final bachelor year...

Here are the questions for the exam.

#TeacherTraining #Pedagogy #ComputerModelling

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ICYMI Big little lies: a compendium and simulation of p-hacking strategies: royalsocietypublishing.org/doi/10.1098/... #RSOS #ComputerModelling

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My thanks to Miriam Glendell for her excellent leadership on this perspective paper on #ComputerModelling to support #SystemsThinking published in Discover Sustainability today. doi.org/10.1007/s436... @hutton.ac.uk @huttonsegs.bsky.social 🧪

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With the aid of 3D glasses, this talk will explore the #materialsscience of lithium #batteries for #electricvehicles, and novel compounds for next-generation #solarcells. We will also explore the use of #computermodelling to allow crystal gazing on the atomic-scale.

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The health-care crisis won’t be solved without addressing the elephant in the room: Staff workload | The-14 Excess workload in healthcare leads to burnout and staff shortages. Evidence-based solutions are needed urgently.

The health-care crisis won’t be solved without addressing the elephant in the room: Staff workload
#Canada #Health #Healthcare #Nurses #Nursing #Work #Overtime #Workload #HealthcareSystem #StaffShortages #ComputerModelling #HealthcareCrisis
the-14.com/the-health-c...

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